
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCAmelCase     :	Optional[Any]       			=  {'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  a__       			=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a__       			=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    a__       			=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    a__       			=						{
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			ZeroShotClassificationPipeline(
        model=UpperCamelCase__     ,				tokenizer=UpperCamelCase__     ,				candidate_labels=["""polics""", """health"""]       )
    return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # No kwarg
    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics, public health"""       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""This text is about {}"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # https://github.com/huggingface/transformers/issues/13846
    __magic_name__								=			classifier(["""I am happy"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(1       )
        ]     ,				)
    __magic_name__								=			classifier(["""I am happy""", """I am sad"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(2       )
        ]     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(""""""     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(UpperCamelCase__     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=""""""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=UpperCamelCase__       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""Not formatting template"""     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template=UpperCamelCase__     ,				)

    self.run_entailment_id(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Pipeline       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			zero_shot_classifier.model.config
    __magic_name__								=			config.labelaid
    __magic_name__								=			zero_shot_classifier.entailment_id

    __magic_name__								=			{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				-1       )

    __magic_name__								=			{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				2       )

    __magic_name__								=			original_labelaid
    self.assertEqual(UpperCamelCase__     ,				zero_shot_classifier.entailment_id       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    # There was a regression in 4.10 for this
    # Adding a test so we don't make the mistake again.
    # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
    zero_shot_classifier(
        """Who are you voting for in 2020?""" * 100     ,				candidate_labels=["""politics""", """public health""", """science"""]       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @require_tf
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""tf"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @slow
  @require_torch
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""pt"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)





  @slow
  @require_tf
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""tf"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

